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Long short-term memory artificial neural network model for prediction of prostate cancer survival outcomes according to initial treatment strategy: development of an online decision-making support system

  • Kyo Chul Koo
  • Kwang Suk Lee
  • Suah Kim
  • Choongki Min
  • Gyu Rang Min
  • Young Hwa Lee
  • Woong Kyu Han
  • Koon Ho Rha
  • Sung Joon Hong
  • Seung Choul Yang
  • Byung Ha ChungEmail author
Original Article

Abstract

Purpose

The delivery of precision medicine is a primary objective for both clinical and translational investigators. Patients with newly diagnosed prostate cancer (PCa) face the challenge of deciding among multiple initial treatment modalities. The purpose of this study is to utilize artificial neural network (ANN) modeling to predict survival outcomes according to initial treatment modality and to develop an online decision-making support system.

Methods

Data were collected retrospectively from 7267 patients diagnosed with PCa between January 1988 and December 2017. The analyses included 19 pretreatment clinicopathological covariates. Multilayer perceptron (MLP), MLP for N-year survival prediction (MLP-N), and long short-term memory (LSTM) ANN models were used to analyze progression to castration-resistant PCa (CRPC)-free survival, cancer-specific survival (CSS), and overall survival (OS), according to initial treatment modality. The performances of the ANN and the Cox-proportional hazards regression models were compared using Harrell’s C-index.

Results

The ANN models provided higher predictive power for 5- and 10-year progression to CRPC-free survival, CSS, and OS compared to the Cox-proportional hazards regression model. The LSTM model achieved the highest predictive power, followed by the MLP-N, and MLP models. We developed an online decision-making support system based on the LSTM model to provide individualized survival outcomes at 5 and 10 years, according to the initial treatment strategy.

Conclusion

The LSTM ANN model may provide individualized survival outcomes of PCa according to initial treatment strategy. Our online decision-making support system can be utilized by patients and health-care providers to determine the optimal initial treatment modality and to guide survival predictions.

Keywords

Artificial intelligence Decision support techniques Prostate cancer Survival 

Abbreviations

ANN

Artificial neural network

AUC

Area under the curve

CRPC

Castration-resistant prostate cancer

CSS

Cancer-specific survival

CV

Cross-validation

LSTM

Long short-term memory

MLP

Multilayer perceptron

MLP-N

MLP for N-year survival prediction

OS

Overall survival

PCa

Prostate cancer

Notes

Acknowledgments

This study was supported through a Young Researcher Program Grant from the National Research Foundation of Korea (NRF-2017R1C1B5017516).

Author contributions

Protocol/project development: Koo, KS Lee, Han, Rha, Hong, Yang and Chung. Data collection and management: Koo, KS Lee, YH Lee, and GR Min. Data analysis: Kim and C Min. Manuscript writing/editing: Koo and Chung.

Compliance with ethical standards

Conflict of interest

All of the authors declare that they have no conflicts of interest to declare.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was not required for the purposes of this study as it was based upon retrospective anonymous patient data and did not involve patient intervention or the use of human tissue samples.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2020

Authors and Affiliations

  • Kyo Chul Koo
    • 1
  • Kwang Suk Lee
    • 1
  • Suah Kim
    • 2
  • Choongki Min
    • 2
  • Gyu Rang Min
    • 1
  • Young Hwa Lee
    • 1
  • Woong Kyu Han
    • 1
  • Koon Ho Rha
    • 1
  • Sung Joon Hong
    • 1
  • Seung Choul Yang
    • 1
  • Byung Ha Chung
    • 1
    Email author
  1. 1.Department of UrologyYonsei University College of MedicineSeoulRepublic of Korea
  2. 2.Selvas AISeoulRepublic of Korea

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